ECONOMETRIC MODEL SPECIFICATION AND D-SEPARATION: A MONTE CARLO EXPERIMENT

Abstract

The purpose of this paper is to identify how Directed Acyclic Graphs perform in a commonly occurring setting in economics, namely the use of proxy variables. When estimating the coefficients that describe the relationships between variables in an economic model, theoretical foundations guide the selection of variables to include in the statistical framework. However, some important variables lack measurable scales. Variables such as education, intelligence, or price expectations are often key explanatory factors, but since they are not directly observable, researchers must choose between treating them as omitted variables or using proxy indicators like years of schooling, test scores, or expected future prices. The method of this research aims to compare the traditional use of proxy variables with the methodology based on Directed Acyclic Graphs. To highlight the distinctions between both approaches, using a Monte Carlo simulation. We were able to demonstrate in a Monte Carlo simulation that the proxy variable suggested by existing literature could be worse than the procedure suggested by Directed Acyclic Graphs, these findings are relevant for the specification of economic models. Our conclusions indicate that dealing with an unobservable variable requires careful consideration when introducing a proxy variable. While earlier studies recommend using a proxy to address the issue of missing variables, the Directed Acyclic Graphs method advises a thorough evaluation of the proxy's attributes beyond merely its strong correlation with the unobservable variable.

Keywords

• Proxy Variables
• Directed Acyclic Graphs
• Monte Carlo Simulation

EKONOMETRİK MODEL BELİRLEME VE D-AYRIMI: BİR MONTE CARLO DENEYİ

Abstract

Bu çalışmanın amacı, ekonomi literatüründe sıklıkla karşılaşılan bir durum olan vekil (proxy) değişkenlerin kullanımı bağlamında Yönlendirilmiş Çevrimsiz Grafiklerin (Directed Acyclic Graphs, DAG) nasıl bir performans sergilediğini incelemektir. Bir ekonomik modelde değişkenler arasındaki ilişkileri tanımlayan katsayılar tahmin edilirken, istatistiksel çerçeveye dâhil edilecek değişkenlerin seçimi kuramsal temellere dayanmaktadır. Ancak bazı önemli değişkenlerin ölçülebilir ölçekleri bulunmamaktadır. Eğitim, zekâ ya da fiyat beklentileri gibi değişkenler çoğu zaman temel açıklayıcı faktörlerdir; ne var ki doğrudan gözlemlenemedikleri için araştırmacılar bu değişkenleri ya dışlanmış değişkenler olarak ele almak ya da öğrenim süresi, test puanları veya beklenen gelecek fiyatlar gibi vekil göstergeler kullanmak arasında bir tercih yapmak zorunda kalmaktadır. Bu çalışmanın yöntemi, vekil değişkenlerin geleneksel kullanımını Yönlendirilmiş Çevrimsiz Grafiklere dayalı metodoloji ile karşılaştırmayı amaçlamaktadır. Her iki yaklaşım arasındaki farkları ortaya koymak için Monte Carlo simülasyonu kullanılmaktadır. Monte Carlo simülasyonu aracılığıyla, mevcut literatür tarafından önerilen vekil değişkenin, Yönlendirilmiş Çevrimsiz Grafikler tarafından önerilen prosedüre kıyasla daha kötü sonuçlar verebileceği gösterilmiştir. Bu bulgular, ekonomik modellerin belirlenmesi açısından önem taşımaktadır. Sonuçlarımız, gözlemlenemeyen bir değişkenle karşılaşıldığında vekil değişkenin modele dâhil edilmesinin dikkatli bir değerlendirme gerektirdiğine işaret etmektedir. Önceki çalışmalar, eksik değişken sorununu gidermek için vekil değişken kullanımını önermekteyken, Yönlendirilmiş Çevrimsiz Grafikler yaklaşımı, vekil değişkenin yalnızca gözlemlenemeyen değişkenle olan yüksek korelasyonuna değil, onun yapısal özelliklerine de kapsamlı biçimde dikkat edilmesini salık vermektedir.

Keywords

• Vekil Değişkenler
• Yönlendirilmiş Çevrimsiz Grafikler
• Monte Carlo Simülasyonu

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